{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.preprocessing as sp\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = np.load('covariance.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_name = ['Society Culture',\n",
    "            'Science Mathematics',\n",
    "            'Health' ,\n",
    "            'Education Reference' ,\n",
    "            'Computers Internet' ,\n",
    "            'Sports' ,\n",
    "            'Business Finance' ,\n",
    "            'Entertainment Music' ,\n",
    "            'Family Relationships' ,\n",
    "            'Politics Government']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4GP9e2HF4uqRBkVpmjqb8CbDf4WqchftPnHjUXyVr/48JkjloIpwluyhwF47e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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f46790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(m, cmap='Blues',)\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "ax = plt.gca();\n",
    "ax.xaxis.tick_top()\n",
    "ax.set_xticks(np.arange(0, 10, 1));\n",
    "ax.set_yticks(np.arange(0, 10, 1));\n",
    "ax.set_xticklabels(class_name, rotation = 45, ha=\"left\",);\n",
    "ax.set_yticklabels(class_name);\n",
    "plt.savefig('Covariance_matrix_yahoo_label.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114229e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cax = plt.imshow(m, cmap='Blues',)\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "ax = plt.gca();\n",
    "plt.xticks([], [])\n",
    "plt.yticks([], [])\n",
    "\n",
    "# Add colorbar, make sure to specify tick locations to match desired ticklabels\n",
    "# cbar = plt.colorbar(cax, ticks=[-0.1, 0.0, 0.1, 0.2,0.3, 0.4, 0.5])\n",
    "# cbar.ax.set_yticklabels(['-0.1', '0.0', '0.1', '0.2', '0.3', '0.4', '0.5'], fontsize=20)# vertically oriented colorbar\n",
    "\n",
    "plt.savefig('Covariance_matrix_yahoo.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
